code: def __init__(
      self,
      base_path,
      train_file=None,
      dev_file=None,
      test_file=None,
      predict_file=None,
      label_file=None,
      label_list=None,
      train_file_with_header=False,
      dev_file_with_header=False,
      test_file_with_header=False,
      predict_file_with_header=False,
      tokenizer=None,
      max_seq_len=128,
  ):
      super(BaseNLPDataset, self).__init__(
          base_path=base_path,
          train_file=train_file,
          dev_file=dev_file,
          test_file=test_file,
          predict_file=predict_file,
          label_file=label_file,
          label_list=label_list,
          train_file_with_header=train_file_with_header,
          dev_file_with_header=dev_file_with_header,
          test_file_with_header=test_file_with_header,
          predict_file_with_header=predict_file_with_header,
      )
      self.tokenizer = tokenizer
      self.max_seq_len = max_seq_len
      self._train_records = None
      self._dev_records = None
      self._test_records = None
      self._predict_records = None
  
imports: import io
  import csv
  import collections
  import numpy
  from tqdm import tqdm
  from paddlehub.compat.datasets.base_dataset import InputExample, BaseDataset
  from paddlehub.utils.log import logger
  from paddlehub.text.tokenizer import CustomTokenizer
  from paddlehub.text.bert_tokenizer import BertTokenizer
label_desc: Use image node_id 1 to create a new BaseNLPDataset object from inherited base classes: BaseDataset with example: obj = BaseNLPDataset(base_path, train_file, dev_file, test_file, predict_file, label_file, label_list, train_file_with_header, dev_file_with_header, test_file_with_header, predict_file_with_header, tokenizer, max_seq_len)